Knowledge Gain in Production Planning and Execution Systems

Abstract

AbstractThe aim of this work is to identify use cases supporting production planning and execution with big data mining in make to order (MTO) based manufacturing companies. MTO manufacturers are described by a variety of characteristics. Due to the production in the context of customer orders, adherence to delivery dates, production quality, flexibility and the knowledge of production managers are key success factors. A suitable production planning and execution system is required. In addition to Workload Control, which was specially developed for MTO manufacturing, manufacturing resource planning, enterprise resource planning or the theory of constraints can also be considered. These systems are collecting increasing amounts of data in the production process. Consultants for production planning systems and production experts were interviewed so as to obtain a deep and integrated understanding of the topic, and in parallel an internal, context-dependent view of the processes. The evaluation using deductive and inductive category based coding shows that Workload Control and Theory of Constraints are not used as production planning and control systems. The theoretically developed use cases were critically discussed and their usefulness for production managers evaluated, while two additional application cases were identified. Furthermore, limitations in the data mining process could be determined.KeywordsProduction planning and executionPredictive data miningKnowledge gain in PPSMake to order manufacturers